A comparison of classification methods for predicting Chronic Fatigue Syndrome based on genetic data

Huang, Lung-Cheng; Hsu, Sen-Yen; Lin, Eugene

doi:10.1186/1479-5876-7-81

Cited by 60 publications

(84 citation statements)

References 36 publications

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“…[3] Discusses techniques that could be employed for feature selection and classification. [4], [5] Discusses the performance of three families of classification algorithms and also brings out the facts that the size of the sample and quality of data play a major role in determining the accuracy. Recent research reveals that deep learning algorithms facilitate modeling high level abstraction [9].…”

Section: Related Workmentioning

confidence: 99%

Safe sonet: a framework for building trustworthy relationships

Neethu¹,

Harini²

2018

IJET

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The advent of easy to use services and the ability to bridge boundaries in space and time has increasingly changed our social lives and com-munication habits. Being unaware of their audience people on social networks inconsiderately share many personal items. Generally Social Networking Sites (SNS) users depend on the SNS platform for managing their social identities. Although SNS providers have introduced privacy settings to protect users against threats, these are insufficient and the access control models are difficult to be understood by novice users [1]. Unexpectedly, even those who are aware of the security and privacy threats and the preventive tools that combat those threats, lack the motivation to utilize security features to protect themselves. The paper discusses a frame-work (Safe SoNet) that aims to provide a plat-form for secure sharing of posts. The objective of this framework is twofold. Firstly, to analyze user behavior by retrieving user information from various social networking sites, addressing transliteration issues. Secondly, apply the user behavior to quantify the trust score of a connect, which would in turn form a decision making parameter to decide on the peer with whom the information can be securely shared.

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Section: Related Workmentioning

confidence: 99%

Safe sonet: a framework for building trustworthy relationships

Neethu¹,

Harini²

2018

IJET

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“…4,5 In addition, we used the receiver operating characteristic (ROC) methodology and calculated the area under the ROC curve (AUC). 13,17 Most researchers have now adopted AUC for evaluating predictive ability of classifiers owing to the fact that AUC is a better performance metric than accuracy. 19 The AUC of…”

Section: Evaluation Of the Predictive Performancementioning

confidence: 99%

“…17 First, the whole dataset was randomly divided into 10 distinct parts. Second, the model was trained by nine-tenths of the data and tested by the remaining tenth of data to estimate the predictive performance.…”

Section: Evaluation Of the Predictive Performancementioning

confidence: 99%

“…17 The wrapper-based approach conducts best-first search for a good subset and uses the classification algorithm itself as part of the function for evaluating feature subsets. 18 The best-first search starts with an empty set of clinical factors and searches forward to choose possible subsets of clinical factors by greedy hill-climbing augmented with a backtracking technique.…”

Section: Feature Selectionmentioning

confidence: 99%

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Pharmacogenomics of drug efficacy in the interferon treatment of chronic hepatitis C using classification algorithms

Hwang²,

Lin³

2010

AABC

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Chronic hepatitis C (CHC) patients often stop pursuing interferon-alfa and ribavirin (IFN-alfa/RBV) treatment because of the high cost and associated adverse effects. It is highly desirable, both clinically and economically, to establish tools to distinguish responders from nonresponders and to predict possible outcomes of the IFN-alfa/RBV treatments. Single nucleotide polymorphisms (SNPs) can be used to understand the relationship between genetic inheritance and IFN-alfa/RBV therapeutic response. The aim in this study was to establish a predictive model based on a pharmacogenomic approach. Our study population comprised Taiwanese patients with CHC who were recruited from multiple sites in Taiwan. The genotyping data was generated in the high-throughput genomics lab of Vita Genomics, Inc. With the wrapper-based feature selection approach, we employed multilayer feedforward neural network (MFNN) and logistic regression as a basis for comparisons. Our data revealed that the MFNN models were superior to the logistic regression model. The MFNN approach provides an efficient way to develop a tool for distinguishing responders from nonresponders prior to treatments. Our preliminary results demonstrated that the MFNN algorithm is effective for deriving models for pharmacogenomics studies and for providing the link from clinical factors such as SNPs to the responsiveness of IFN-alfa/RBV in clinical association studies in pharmacogenomics.

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“…Lung-Cheng Huang reported that Naïve Bayesian classifier produces high performance than SVM and C 4.5 for the CDC Chronic fatigue syndrome dataset [14]. Paul R Harper [12] reported that there is not necessary a single best classification tool but instead the best performing algorithm will depend on the features of the dataset to be analyzed.…”

Section: Introductionmentioning

confidence: 99%